Introduction to Data Visualization with Plotly in Python
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Learn to build, style and add interactive controls to Plotly charts in Python, including subplots, time buttons, dropdowns and sliders.
Interactive charts let readers hover over a point, zoom into a stretch of dates or switch between views, but building that behavior used to require complex code. Plotly brings it into Python. You build common chart types, style them with color, annotations and custom axes, and then add controls such as time buttons, dropdowns and sliders that change what a figure shows.
What You Will Achieve
- Build bar charts, box plots and histograms with Plotly and use color to compare groups side by side
- Make a scatterplot or line chart easier to read with hover details, legends, notes on key points and log-scale axes
- Combine several charts in one figure, either as a grid of subplots or as a line drawn over bars
- Give viewers buttons, dropdowns and sliders so they can jump to a date range or switch what the chart displays
Program Overview
Plotly Figures and Univariate Plots
Starts with how a Plotly figure is created, edited and fixed, then moves to single-variable charts and their color settings.
- Creating and editing a Plotly figure
- Bar graphs, box plots and histograms
- Color options and stacked histogram bars
Bivariate Plots and Plot Customization
Covers charts that relate two variables and the settings that make them clearer to readers.
- Scatterplots and line plots
- Hover information and legends
- Annotations
- Axis titles and log scales
Advanced Customization and Interactivity
Brings several charts into one figure and adds controls that let viewers reshape the view on demand.
- Subplots and layered charts
- Time buttons for periods such as year to date
- Custom buttons and dropdowns
- Sliders and hiding a trace
Certificate Awarded (Paid)
Complete the course to earn it.
Job Outlook
These charting skills apply wherever data is reported to other people, including analytics, reporting and data science work, where interactive figures let readers explore results themselves.
Good for: Python users who already work with data and want charts that readers can hover over, zoom and filter without leaving Python.
Keep in mind: Despite the Beginner label, the last two chapters spend most of their time on advanced customization: layered charts, time buttons, dropdowns and sliders.
Bottom line: Four focused chapters that take Plotly charts past styling into buttons, dropdowns and sliders that change the figure on demand.
Frequently Asked Questions
Which chart types does the course build?
The early chapters work with bar graphs, box plots, histograms, scatterplots and line plots. Later lessons combine them as subplots or layer one chart on another, such as a line chart drawn over a bar chart in the same figure.
What kinds of data do the exercises use?
The practice datasets include student scores, company revenues, GDP and life expectancy, bird features, basketball statistics, savings, rainfall, temperatures, housing prices and finance data, so each chart type is tried on a concrete example.
What are Plotly time buttons used for?
Time buttons are selectors added to a line chart that switch the view to a set period, such as year to date, so readers can zoom in and out. The lessons build a button dictionary and apply it to rainfall and finance line charts.
What software does the course use?
All the work is done in Python with the Plotly charting library. The setup and installation steps come at the start of the course.
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